PLoS One公共科学图书馆·综合
PLoS One(英文缩写 PLOS ONE),ISSN 1932-6203,eISSN 1932-6203,中文译名:公共科学图书馆·综合 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
PLoS One 最新收录文献
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1. A new approach of a path-tracking system of a differential drive robot for an arbitrary set of waypoints using sliding mode control.
PMID:日期:2026-10-02Autonomous differential-drive robots are typically controlled to follow a desired path. This path may be defined by a continuous curve or set of waypoints. Few studies have examined cases in which a path is defined by an arbitrary set of waypoints. This paper introduces a new approach to a path-tracking system for an arbitrary set of waypoints using a sliding mode control (SMC) system. This approach is based on a nonlinear kinematic model of the tracking error derived in this research for a differential-drive robot. Based on the derived error model, a sliding mode controller is introduced to drive the tracking error to zero. The stability of the system was also investigated. In addition, the effects of robot dynamics were incorporated into the proposed controller without prior knowledge of the exact parameters of the robot. Simulations were conducted to compare the proposed control system with a widely used approach to demonstrate the effectiveness of the proposed controller in tight maneuvers, particularly at directional discontinuity and/or sharp turns. The proposed technique was verified through real-time experimental tests using a differential drive robot.
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2. Protocol for the REVELIO test-track pilot study: A randomised, controlled, single-centre trial in healthy recreational cannabis users investigating real-time in-vehicle detection of cannabis-impaired driving.
PMID:日期:2026-10-01Driving under the influence of cannabis is associated with impaired cognitive and psychomotor performance and an increased risk of traffic accidents. Reliable real-time in-vehicle systems for detecting cannabis-related driving impairment are currently lacking; but hold great potential for improving road safety. This protocol describes the REVELIO test-track pilot study: a randomised, controlled, open-label, interventional, single-centre trial. The study assesses the feasibility and methodological requirements for developing and evaluating a multimodal in-vehicle detection approach using vehicle and driver state data. A total of 45 healthy recreational cannabis users will be enrolled and randomly allocated to an intervention or a reference control group. During the main study day, all participants will undergo biological sampling for tetrahydrocannabinol (THC) and related metabolites, as well as pre-driving assessments, followed by a sober baseline driving session on a closed test track using a dual-pedal vehicle with a certified driving instructor onboard. Participants in the intervention group will then receive a single controlled inhalative cannabis dose (target 0.67 mg THC per kg body weight), while the reference group will receive no cannabis. All participants will subsequently complete three additional standardized 50-minute driving sessions at predefined time points up to approximately six hours after administration, following identical schedules to enable within- and between-group comparisons. Between driving sessions, structured breaks will include recovery periods, repeated biological sampling, and traffic-medical, traffic-psychological, and pre-driving performance assessments, to characterise the temporal dynamics of cannabis-related impairment. Multimodal data will be collected, including vehicle controller area network (CAN) data, driver monitoring camera (DMC) data, physiological signals using wearables, and biological samples (capillary blood, breath, oral fluid. Machine-learning-based models will be developed and evaluated to distinguish sober from cannabis-influenced driving states under controlled conditions. Secondary analyses will examine changes in driving performance over time and associations between functional measures and biological THC concentrations. As an exploratory pilot study conducted on a secured test track, the protocol aims to generate standardized reference data and quantitative performance metrics to inform both feasibility and system design considerations. The study is registered at ClinicalTrials.gov (NCT07401628).
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3. Energy infrastructure construction and enterprises' Artificial Intelligence development: Evidence from China's ultra-high voltage transmission projects.
3. 能源基础设施建设与企业人工智能发展：来自中国特高压输电项目的证据PMID:日期:2026-10-01Ultra-high voltage (UHV) transmission projects are a key component of China's energy supply-side reforms, improving the efficiency and reliability of the power system. While their macro-level importance is well recognized, evidence on their impact on firm-level artificial intelligence (AI) development remains limited. This study addresses this gap by exploiting the staggered rollout of UHV transmission projects across prefecture-level cities as a quasi-natural experiment. Using a multi-period difference-in-differences (DID) approach on a panel of Chinese listed enterprises from 2007 to 2023, this study finds that UHV transmission projects significantly promote enterprises' AI development. The effect is stronger for non-state-owned enterprises, enterprises located in inland regions, enterprises with CEO-chair duality, and high-tech enterprises. Mechanism analyses show that UHV transmission projects enhance enterprises' AI development by strengthening technological innovation, expanding enterprises' organizational resource base, and improving regional financial development. Further analyses indicate that UHV transmission projects also improve resource allocation efficiency and alleviate development bottlenecks. By linking physical energy infrastructure with firm-level AI development, this study highlights the importance of reliable electricity supply for technology adoption and digital transformation.
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4. Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks.
4. 基于ISM和贝叶斯网络的采矿工程研究生教育高质量发展评价PMID:日期:2026-10-01The quality of graduate education in mining engineering is important for safeguarding national energy security and supporting the green and intelligent transformation of the mining industry. This study developed an evaluation system comprising five criterion-level dimensions, 12 sub-criteria, and 26 indicators. Interpretive Structural Modeling (ISM) was used to identify the hierarchical relationships among the indicators, and an expert-informed Bayesian network was subsequently established for probabilistic evaluation. The results showed that the target node had a positive-state probability of 47%, indicating an overall medium level of development. Curriculum quality exhibited the highest positive-state probability among the sub-criteria. When the target node was set to the positive state, the core curriculum renewal cycle, supervision frequency in practical training, and supervisors' technological updating capability showed comparatively high posterior probabilities, whereas research platform support emerged as a relatively weak node requiring further attention. These findings provide structured decision-support evidence for education administrators and policymakers seeking to improve the quality of graduate education in mining engineering in China.
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5. Newton-type inequalities for the class of modified hyperbolic p-convex functions via Riemann-Liouville fractional integrals with numerical justifications.
PMID:日期:2026-10-01In this paper, an identity involving Riemann-Liouville fractional integrals is established which serves as the basis for several new Newton-type inequalities involving the modified class of hyperbolic p-convex property of differentiable functions. Some new inequalities are also derived for functions of bounded variation. Examples along with graphs are provided to demonstrate the validity of the newly established inequalities, and the comparison with earlier results highlights their enhanced applicability and significance.
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6. Informed forecasting: Leveraging auxiliary knowledge to boost large language model performance on time series forecasting.
PMID:日期:2026-10-01The rapid adoption of large language models (LLMs) has sparked growing interest in extending their capabilities beyond traditional natural language tasks, including applications in time series forecasting. This work explores the enhancement of time series forecasting with LLMs by incorporating time-dependent covariates. We propose a set of representative prompting strategies that span a wide range of formats while incorporating covariates, and then implement the proposed framework to evaluate performance across three real-world time series datasets in healthcare, service operations, and transportation. In our experiments, we use GPT-4o-mini and show that incorporating relevant covariate information through an appropriate prompt design can substantially improve forecasting accuracy, reducing prediction errors by up to 70% compared with the no-covariate baseline. Furthermore, the analysis reveals that both the choice of covariate and the prompt structure are critical, as poorly aligned configurations may degrade performance. Finally, we conduct sensitivity analyses to assess the effect of covariate integration in censored settings and quantify uncertainty, confirming that our method achieves statistically significant improvements over existing approaches. These findings underscore the potential of covariate integration in prompt design to bridge the gap between general-purpose LLMs and forecasting tasks.
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7. A mechanism-guided simulation framework for synthetic tax-risk data: Constraint validation and reproducible benchmarking.
PMID:日期:2026-10-01Synthetic benchmarks can support tax-risk method development when administrative records are inaccessible, but a simulator can also predetermine the patterns later reported as detection findings. We present the Tax Data Generation Model (TDGM), a mechanism-guided simulator that enforces specified income-statement and balance-sheet identities, generates a three-year panel for 10,000 firms, and injects four prespecified risk strategies. This study uses no administrative audit data; its purpose is to test whether the implementation reproduces its design assumptions and to provide a reproducible benchmark. All predictive evaluations used five repeated 70/30 group splits by firm, so no firm appeared in both training and test data. Observed marginal effects were heterogeneous rather than uniformly small: for all risky observations, Cohen's d was -1.29 for profit margin, -1.20 for tax burden, 1.05 for the transfer-pricing indicator, and 0.15 for the income-gap ratio. In a controlled cross-sectional comparison, raw CTGAN failed both row-level accounting identities, whereas deterministic projection restored 100% validity but worsened dependence fidelity. Under the revised classifier specification, a one-hidden-layer neural network with conventional L2 regularization achieved mean ROC-AUC 0.9942 and PR-AUC 0.9710; XGBoost achieved 0.9714 and 0.8975, respectively. Removing the income-gap ratio changed XGBoost ROC-AUC only from 0.9714 to 0.9702, while restricting the model to raw Level 1 variables reduced it to 0.8941. At 1% prevalence, XGBoost ROC-AUC remained 0.9720 but PR-AUC fell to 0.6703, showing why prevalence-sensitive metrics are required. Together, the three contributions are a constraint-by-construction simulator, a reusable audit protocol for mechanism-guided generators, and explicit measurement of the accounting-repair/statistical-fidelity trade-off. Because selected flows are changed after stock generation, derived ratios can carry design-induced information. These are internal properties of a synthetic data-generating process, not estimates of real tax-evasion behavior or deployable audit performance.
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8. OQ-FERNet: An occlusion-aware quantitative facial expression recognition network for real-time human-computer interaction.
PMID:日期:2026-10-01Facial expression recognition is an important task in real-time human-computer interaction, but its practical application is still limited by partial facial occlusion, high computational cost, and the lack of fine-grained emotion representation. To address these issues, this paper proposes OQ-FERNet, an occlusion-aware quantitative facial expression recognition network. The proposed network adopts MobileNetV4-Conv-S as a lightweight facial expression feature extraction backbone to obtain compact and discriminative representations with low computational cost. On this basis, an Occlusion-aware Regional Reweighting module is designed to divide the feature map into upper-face, middle-face, and lower-face regions, and adaptively adjust their contributions according to regional reliability. This design suppresses occluded or less informative facial areas while enhancing visible expression-related cues. Furthermore, a Quantitative Emotion Prediction Head is introduced to jointly perform discrete expression classification and emotion intensity estimation, enabling the model to provide both categorical and fine-grained affective outputs. Experiments are conducted on RAF-DB and AffectNet-7. The results show that OQ-FERNet achieves competitive classification performance, improved robustness under different occlusion conditions, and effective quantitative emotion prediction. Specifically, OQ-FERNet obtains 93.12% accuracy on RAF-DB and 68.32% accuracy on AffectNet-7, while achieving an MAE of 0.246, an MSE of 0.101, and an RMSE of 0.318 for quantitative emotion prediction on AffectNet-7. In addition, the model contains only 4.18M parameters and 0.26G FLOPs, with an inference speed of 356 FPS. These results indicate that OQ-FERNet provides an effective and efficient solution for lightweight, occlusion-robust, and fine-grained facial expression recognition in real-time human-computer interaction scenarios. The source code is publicly available at: https://github.com/jiahao001-j/OQ-FERNet.
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9. Stakeholder perspectives on adolescent sexual and reproductive health service utilization in Uganda's Busoga region: A qualitative study.
PMID:日期:2026-10-01Adolescents in low- and middle-income countries face high rates of unintended pregnancy, sexually transmitted infections (STIs), and unmet sexual and reproductive health (SRH) needs. In Uganda, 24% of girls aged 15-19 years have begun childbearing, yet modern contraceptive prevalence among adolescents remains low (15%). These challenges are particularly severe in the Busoga region, where sociocultural norms, poverty, and limited health system capacity hinder access to adolescent-friendly SRH services. Despite national guidelines, utilization remains low. Few studies have triangulated perspectives from adolescents, health providers, and community/religious leaders. We explored stakeholder views to provide an integrated understanding of factors influencing adolescent SRH service utilization in Busoga, Uganda. An exploratory qualitative study was conducted in Iganga and Bugweri districts. Four focus group discussions with 38 adolescents (13-19 years), stratified by sex, age group, and residence, and 12 key informant interviews with health providers and community leaders were conducted. Adolescents were recruited through peer networks, while key informants were purposively selected. Data were collected (November-December 2024), transcribed verbatim, and analyzed inductively using thematic analysis with NVivo 14. Ethical approval and informed consent/assent were obtained. Adolescents, health providers, and community stakeholders described SRH service utilization as shaped by adolescent motivation alongside persistent barriers. Adolescents showed strong preventive intent, but uptake was limited by misconceptions about contraception, fears of infertility, long distances, lack of privacy, negative provider attitudes, and commodity stock-outs. Providers and community leaders echoed concerns around misinformation, limited parental guidance, moral norms, and structural constraints. All stakeholders converged on the need for confidential, accessible, and peer- and community-based adolescent SRH services. Adolescents in Busoga are motivated to use SRH services, but intersecting sociostructural and system barriers limit utilization. Strengthening adolescent-friendly services, improving provider responsiveness, addressing misinformation, and expanding community-based delivery are essential to enhance equitable access.
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10. A domain knowledge-enhanced lightweight transformer for automatic identification of land-use conflicts in territorial spatial planning texts.
PMID:日期:2026-10-01Implicit information on land use conflicts in territorial spatial planning texts directly affects the coordination and implementability of planning schemes. Traditional manual interpretation approaches have low efficiency, high omission rates, and inadequate standardization, making them difficult to use for full-process review of massive multi-source planning texts under multi-plan integration. To address this problem, this study proposes a lightweight Transformer-based framework for automatic land use conflict identification by integrating domain prior features with deep semantic encoding. Multi-type texts related to territorial planning were collected from official and authoritative channels to construct a domain-specific dataset containing 1200 valid samples. A multi-dimensional feature system with strong discriminability and interpretability was then extracted by combining professional knowledge of territorial planning with text statistical attributes. Finally, a lightweight Transformer model with a total parameter count of only 700000 was designed. Through the deep fusion of textual semantic features and statistical features, the model performs binary classification of land use conflicts. Experimental results show that the proposed model achieved an accuracy of 98.75% and an F1-score of 96.9% on the held-out test set, with an inference time of less than 0.5 seconds for a single text. On the 240-sample held-out test set, the 95% confidence interval for accuracy was 96.39-99.57%, and bootstrap resampling gave a 95% interval of 92.93-100.00% for the F1-score, indicating that the point estimates were not driven by a small number of individual cases. The framework may support lightweight local deployment in grassroots planning management departments after site-specific validation and can provide an auxiliary tool for planning compilation, compliance review, and ex-ante early warning of conflicts in territorial spatial planning.